In the remote sensing/aerial imaging industry, imagery is used to capture views of a geographic area and be able to measure objects and structures within the images as well as to be able to determine geographic locations of points within the image. These are generally referred to as “geo-referenced images” and come in two basic categories:                1. Captured Imagery—these images have the appearance they were captured by the camera or sensor employed.        2. Projected Imagery—these images have been processed and converted such that they confirm to a mathematical projection.        
All imagery starts as captured imagery, but as most software cannot geo-reference captured imagery, that imagery is then reprocessed to create the projected imagery. The most common form of projected imagery is the ortho-rectified image. This process aligns the image to an orthogonal or rectilinear grid (composed of rectangles). The input image used to create an ortho-rectified image is a nadir image—that is, an image captured with the camera pointing straight down. It is often quite desirable to combine multiple images into a larger composite image such that the image covers a larger geographic area on the ground. The most common form of this composite image is the “ortho-mosaic image” which is an image created from a series of overlapping or adjacent nadir images that are mathematically combined into a single ortho-rectified image.
When creating an ortho-mosaic, this same ortho-rectification process is used, however, instead of using only a single input nadir image, a collection of overlapping or adjacent nadir images are used and they are combined to form a single composite ortho-rectified image known as an ortho-mosaic. In general, the ortho-mosaic process entails the following steps:                A rectilinear grid is created, which results in an ortho-mosaic image where every grid pixel covers the same amount of area on the ground.        The location of each grid pixel is determined from the mathematical definition of the grid. Generally, this means the grid is given an X and Y starting or origin location and an X and Y size for the grid pixels. Thus, the location of any pixel is simply the origin location plus the number of pixels times the size of each pixel. In mathematical terms: Xpixel=Xorigin+Xsize×Columnpixel and Ypixel=Yorigin+Ysize×Rowpixel.        The available nadir images are checked to see if they cover the same point on the ground as the grid pixel being filled. If so, a mathematical formula is used to determine where that point on the ground projects up onto the camera's pixel image map and that resulting pixel value is then transferred to the grid pixel.        
Because the rectilinear grids used for the ortho-mosaic are generally the same grids used for creating maps, the ortho-mosaic images bear a striking similarity to maps and as such, are generally very easy to use from a direction and orientation standpoint.
However, since they have an appearance dictated by mathematical projections instead of the normal appearance that a single camera captures and because they are captured looking straight down, this creates a view of the world to which we are not accustomed. As a result, many people have difficulty determining what it is they are looking at in the image. For instance, they might see a yellow rectangle in the image and not realize what they are looking at is the top of a school bus. Or they might have difficulty distinguishing between two commercial properties since the only thing they can see of the properties in the ortho-mosaic is their roof tops, where as most of the distinguishing properties are on the sides of the buildings. An entire profession, the photo interpreter, has arisen to address these difficulties as these individuals have years of training and experience specifically in interpreting what they are seeing in nadir or orthomosaic imagery.
Since an oblique image, by definition, is captured at an angle, it presents a more natural appearance because it shows the sides of objects and structures—what we are most accustomed to seeing. In addition, because oblique images are not generally ortho-rectified, they are still in the natural appearance that the camera captures as opposed to the mathematical construction of the ortho-mosaic image.
This combination makes it very easy for people to look at something in an oblique image and realize what that object is. Photo interpretation skills are not required when working with oblique images. Oblique images, however, present another issue. Because people have learned navigation skills on maps, the fact that oblique images are not aligned to a map grid, like ortho-mosaic images, makes them much less intuitive when attempting to navigate or determine direction on an image. When an ortho-mosaic is created, because it is created to a rectilinear grid that is generally a map grid, the top of the orthomosaic image is north, the right side is east, the bottom is south, and the left side is west. This is how people are generally accustomed to orienting and navigating on a map. But an oblique image can be captured from any direction and the top of the image is generally “up and back,” meaning that vertical structures point towards the top of the image, but that the top of the image is also closer to the horizon.
However, because the image can be captured from any direction, the horizon can be in any direction, north, south, east, west, or any point in between.
If the image is captured such that the camera is pointing north, then the right side of the image is east and the left side of the image is west. However, if the image is captured such that the camera is pointing south, then the right side of the image is west and the left side of the image is east. This can cause confusion for someone trying to navigate within the image. Additionally, because the ortho-mosaic grid is generally a rectilinear grid, by mathematical definition, the four cardinal compass directions meet at right angles (90-degrees). But with an oblique image, because it is still in the original form the camera captured and has not been re-projected into a mathematical model, it is not necessarily true that the compass directions meet at right angles within the image.
Because in the oblique perspective, you are moving towards the horizon as you move up in the image, the image covers a wider area on the ground near the top of the image as compared to the area on the ground covered near the bottom of the image. If you were to paint a rectangular grid on the ground and capture it with an oblique image, the lines along the direction the camera is pointing would appear to converge in the distance and the lines across the direction of the camera is pointing would appear to be more widely spaced in the front of the image than they do in the back of the image. This is the perspective view we are all used to seeing—things are smaller in the distance than close up and parallel lines, such as railroad tracks, appear to converge in the distance. By contrast, if an ortho-mosaic image was created over this same painted rectangular grid, it would appear as a rectangular grid in the ortho-mosaic image since all perspective is removed as an incidental part of the ortho-mosaic process.
Because a rectilinear grid is used to construct an ortho-mosaic image and the ortho-mosaic images are manufactured to provide a consistent perspective (straight down) it is possible to create the appearance of a continuous, seamless image by simply displaying such images adjacent to one another, or by overlapping them. Such an appearance allows users to remain oriented when navigating between images since the appearance and orientation of a road, building, or other feature remains very similar from one image to the next.
Because oblique images are not grid aligned and can be captured from any direction a feature on one image will not look the same on another image. This is due to several factors:                The oblique perspective means that image pixels are not aligned on a rectangular grid but are aligned to the perspective that matches the perspective the camera has captured.        The image pixels on an oblique image cannot measure the same size on the ground due to the perspective of the image. The area of ground covered by the pixels in the foreground of the image is smaller than the area of ground covered in the background of the image. Features in the foreground of an image will be a different size in the background of another image.        The effect of building lean varies from image to image.        The direction an edge of a feature travels across an image, such as the edge of a road or building, will vary between images.        The captured portion of a feature will vary between images. For example: a particular image may contain the entire side of a building due to the head-on position of the capturing camera, but another image only captures a portion of the same side due to the capturing camera being at an angle relative to that side.        
Because of these issues, the common practice in the industry is to provide oblique imagery as a series of individual images within which the user can pan. To see beyond the limits of an image the user needs to cause another image to be loaded; they can then pan within that image. This invention details a means to allow continuous panning of oblique images such that the above limitations are overcome.